Table 1:

Comparison of linear and quadratic models using latency index as the Predictor

	Predictor	b	b 95% CI [LL, UL]	beta	beta 95% CI [LL, UL]	Model Fit	Difference between Models
WT	Linear Model
	Day Latency	−0.03**	[−0.05, −0.02]	−0.57	[−0.86, −0.28]	R2 = 0.328** 95% CI[0.09,0.52]
	Quadratic Model
	Day Latency	−0.06*	[−0.12, −0.00]	−1.12	[−2.17, −0.08]	R2 = 0.353**	ΔR2 = 0.025
	Day Latency2	0.01	[−0.00, 0.02]	0.57	[−0.47, 1.62]	95% CI[0.08,0.53]	95% CI[−0.06, 0.11]
HET	Linear Model
	Day Latency	−0.07**	[−0.12, −0.03]	−0.48	[−0.79, −0.17]	R2 = 0.233** 95% CI[0.03,0.44]
	Quadratic Model
	Day Latency	−0.34**	[−0.48, −0.19]	−2.20	[−3.14, −1.26]	R2 = 0.477**	ΔR2 = 0.244**
	Day Latency2	0.05**	[0.02, 0.08]	1.78	[0.84, 2.72]	95% CI[0.19,0.63]	95% CI[0.02, 0.46]

Note. b represents unstandardized regression weights. beta indicates the standardized regression weights. LL and UL indicate the lower and upper limits of a confidence interval, respectively.

^*indicates p < 0.05.

^**indicates p < 0.01.